Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO2

Pendall, Elise; Ogle, Kiona; Parton, William

doi:10.2172/1239688

Title: Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO₂

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Abstract

This research project improved understanding of how climate change (elevated atmospheric CO₂, warming and altered precipitation) can affect grassland ecosystem productivity and nutrient availability. Our advanced experimental and modeling methods allowed us to test 21 specific hypotheses. We found that ecosystem changes over years of exposure to climate change can shift the plant communities and potentially make them more resilient to future climate changes. These changes in plant communities may be related to increased growth of belowground roots and enhanced nutrient uptake by some species. We also found that climate change can increase the spread of invasive and noxious weeds. These findings are important for land managers to make adaptive planning decisions for domestic livestock production in response to climate variability in semi-arid grasslands.

Authors:

Pendall, Elise ^[1]; Ogle, Kiona ^[1]; Parton, William ^[1]

Univ. of Wyoming, Laramie, WY (United States)

Publication Date:: Mon Feb 29 00:00:00 EST 2016

Research Org.:: Univ. of Wyoming, Laramie, WY (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Contributing Org.:: Arizona State Univ., Tempe, AZ (United States); Colorado State Univ., Fort Collins, CO (United States)

OSTI Identifier:: 1239688

Report Number(s):: Final Report: DOE-Wyoming-0006373
03077666293

DOE Contract Number:: SC0006973

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES

Citation Formats


                    Pendall, Elise, Ogle, Kiona, and Parton, William. Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO2.  United States: N. p., 2016. 
        Web.  doi:10.2172/1239688.

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                    Pendall, Elise, Ogle, Kiona, & Parton, William. Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO2.  United States.  https://doi.org/10.2172/1239688

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                    Pendall, Elise, Ogle, Kiona, and Parton, William. 2016.  
        "Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO2".  United States.  https://doi.org/10.2172/1239688.  https://www.osti.gov/servlets/purl/1239688.

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@article{osti_1239688,

  title        = {Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO2},

  author       = {Pendall, Elise and Ogle, Kiona and Parton, William},

  abstractNote = {This research project improved understanding of how climate change (elevated atmospheric CO2, warming and altered precipitation) can affect grassland ecosystem productivity and nutrient availability. Our advanced experimental and modeling methods allowed us to test 21 specific hypotheses. We found that ecosystem changes over years of exposure to climate change can shift the plant communities and potentially make them more resilient to future climate changes. These changes in plant communities may be related to increased growth of belowground roots and enhanced nutrient uptake by some species. We also found that climate change can increase the spread of invasive and noxious weeds. These findings are important for land managers to make adaptive planning decisions for domestic livestock production in response to climate variability in semi-arid grasslands.},

  doi          = {10.2172/1239688},

  url          = {https://www.osti.gov/biblio/1239688},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Feb 29 00:00:00 EST 2016},

  month        = {Mon Feb 29 00:00:00 EST 2016}

}

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https://doi.org/10.2172/1239688

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